Source: Remote Sensing. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CLASSIFICAÇÃO DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, HORIZONTES DO SOLO, SENSORIAMENTO REMOTO
ABNT
HAUBERT, Daiane de Fatima da Silva et al. Soil suborder discrimination using machine learning is improved by SWIR imaging compared with full VIS–NIR–SWIR spectra. Remote Sensing, v. 18, p. 1-26, 2026Tradução . . Disponível em: https://doi.org/10.3390/rs18060898. Acesso em: 23 abr. 2026.APA
Haubert, D. de F. da S., Vedana, N. G., Mendonça, W. A., Oliveira, K. M. de, Oliveira, C. A. de, Gonçalves, J. V. F., et al. (2026). Soil suborder discrimination using machine learning is improved by SWIR imaging compared with full VIS–NIR–SWIR spectra. Remote Sensing, 18, 1-26. doi:10.3390/rs18060898NLM
Haubert D de F da S, Vedana NG, Mendonça WA, Oliveira KM de, Oliveira CA de, Gonçalves JVF, Demattê JAM, Oliveira RB de, Reis AS, Falcioni R, Nanni MR. Soil suborder discrimination using machine learning is improved by SWIR imaging compared with full VIS–NIR–SWIR spectra [Internet]. Remote Sensing. 2026 ; 18 1-26.[citado 2026 abr. 23 ] Available from: https://doi.org/10.3390/rs18060898Vancouver
Haubert D de F da S, Vedana NG, Mendonça WA, Oliveira KM de, Oliveira CA de, Gonçalves JVF, Demattê JAM, Oliveira RB de, Reis AS, Falcioni R, Nanni MR. Soil suborder discrimination using machine learning is improved by SWIR imaging compared with full VIS–NIR–SWIR spectra [Internet]. Remote Sensing. 2026 ; 18 1-26.[citado 2026 abr. 23 ] Available from: https://doi.org/10.3390/rs18060898
